Course Title: Analytics in Industry 2

Part A: Course Overview

Course Title: Analytics in Industry 2

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2303

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 2 2019,
Sem 2 2020,
Sem 2 2021,
Sem 2 2022

Course Coordinator: Dr Yan Wang

Course Coordinator Phone: +61 3 9925 2381

Course Coordinator Email: yan.wang@rmit.edu.au

Course Coordinator Location: 015.04.005

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

It is assumed that students have gained knowledge in mathematics, statistics, data management or/and business information, from their first few years studies with the Analytics program.


Course Description

This course includes a Work Integrated Learning experience in which your knowledge and skills will be applied and assessed in a real or simulated workplace context and where feedback from industry and/or community is integral to your experience.

The course is about the application of analytics, statistics and operations research in a real world situation. You will learn how to think about data in a broad context and what goes on in an industrial research project. You will be taught how to improve your verbal and written communication skills, organise the structure of an industrial research problem and learn about professional ethics.

This course can take the form of a broad range of activities including group or individual projects, work placements, simulated work placements or a combination of the formers.

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information.)


Objectives/Learning Outcomes/Capability Development

 

This course contributes to the following Program Learning Outcomes for BH119 Bachelor of Analytics.

Personal and Professional Awareness

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions.

Knowledge and Technical Competence

  • coherent and advanced knowledge of the underlying principles and concepts in one or more disciplines

Problem Solving

  • cognitive skills to review critically, analyse, consolidate and synthesise knowledge to identify and provide solutions to complex problems with intellectual independence.

Teamwork and Project Management

  • the ability to contribute to professional work settings through effective participation in teams and organisation of project tasks.

Communication

  • communication skills to present clear and coherent exposition of knowledge and ideas to a variety of audiences.

Information Literacy

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.

Ethics

  • the ability to reflect on experience and improve your own future practice.


Course Learning Outcomes (CLOs)

On completion of this course you should be able to:

  1. Propose and justify solutions to problems both familiar and unfamiliar and identify relevant solutions-focused strategies.
  2. Construct and express logical arguments and work in abstract or general terms to increase the clarity and efficiency of your analyses.
  3. Collaborate in a team through interactions with your peers, distinguishing between ethical collaboration, which is strongly encouraged, and plagiarism, which is prohibited.
  4. Present technical solutions to a variety of audiences using high level oral and written skills.
  5. Manage your time, balance competing commitments and meet deadlines for both team-based and individual tasks  to be submitted throughout the semester.


Overview of Learning Activities

You will participate in a group project or industry placement under the supervision of both the academic staff and the supervisor of a partner institution representative (for work placement). You are required to formally define the project requirements and directions based on the available information; conduct a literature review on relevant topics; apply knowledge gained from previous studies; solve real problems; have meetings with your supervisors to receive ongoing feedback; present progress reports and project outcomes to industry contacts and other students; write a detailed report of your research project and submit it for review.


Overview of Learning Resources

 

RMIT library resources including online access resources will be critical especially for the early stages of the project.

You will receive ongoing feedback from industry, other students and your supervisor(s) throughout your project.

Library Subject Guide for Mathematics & Statistics http://rmit.libguides.com/mathstats


Overview of Assessment

Note: This course does not have hurdles.

Assessment Tasks

Assessment Task 1: WIL Ready and Project Proposal
Weighting 30%
This assessment task supports CLOs 1 to 5.

Assessment Task 2: Project Progress Presentation
Weighting 15%
This assessment task supports CLOs 3, 4 and 5.

Assessment Task 3: Final Presentation
Weighting 15%
This assessment task supports CLOs 3, 4 and 5.

Assessment Task 4: Final Report
Weighting 40%
This assessment task supports CLOs 1 to 5.